用于联合尖峰和波形解码的计算高效的模型选择
Computationally efficient model selection for joint spikes and waveforms decoding
摘要：
用于从神经元集合解码行为变量或刺激的最近范例依赖于电极尖峰序列及其波形的联合模型，原则上，这比单独从电极尖峰序列或从分类的神经元尖峰序列解码更有效。在本文中，我们解码的速度手臂达到的猕猴表明，包括波形特征不加区别地在联合解码模型可以贡献更多的噪声和偏见比有用的信息的运动学，从而降低解码性能。我们还表明，选择哪些波形特征应该进入模型，以降低预测风险，可以大大提高解码性能。对于此处分析的数据，对低风险电极尖峰和波形联合模型的逐步搜索产生了低风险贝叶斯模型，该模型比仅基于电极尖峰序列的相应风险最小化贝叶斯模型效率高30%。该联合模型对于仅基于排序的神经元尖峰序列和散列从风险最小化模型进行解码也是非常有效的，证实了先前的结果，即可以在解码应用中消除有问题的尖峰排序步骤。我们能够通过一个大的模型空间搜索低风险的联合模型，这要归功于一个捷径公式，它在基于高斯线性观测方程的模型逐步搜索中加速了大型矩阵求逆。
英文摘要：
A recent paradigm for decoding behavioral variables or stimuli from neuron ensembles relies on joint models for electrode spike trains and their waveforms, which, in principle, is more efficient than decoding from electrode spike trains alone or from sorted neuron spike trains. In this paper, we decode the velocity of arm reaches of a rhesus macaque monkey to show that including waveform features indiscriminately in a joint decoding model can contribute more noise and bias than useful information about the kinematics, and thus degrade decoding performance. We also show that selecting which waveform features should enter the model to lower the prediction risk can boost decoding performance substantially. For the data analyzed here, a stepwise search for a low risk electrode spikes and waveforms joint model yielded a low risk Bayesian model that is 30% more efficient than the corresponding risk minimized Bayesian model based on electrode spike trains alone. The joint model was also comparably efficient to decoding from a risk minimized model based only on sorted neuron spike trains and hash, confirming previous results that one can do away with the problematic spike sorting step in decoding applications. We were able to search for low risk joint models through a large model space thanks to a short cut formula, which accelerates large matrix inversions in stepwise searches for models based on Gaussian linear observation equations.
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